Acoustic Emission or stress wave activity is most commonly associated with the high frequency component of structure borne elastic waves associated with operating machinery and is produced as a result of friction, impacts, cavitation, metal removal, crack growth, plastic deformation and other energy loss processes taking place during the operation of the machinery.
Noise signals are airborne sound waves associated with operating machinery and are produced as a result of out of balance forces, friction, impacts, cavitation, metal removal, crack growth, plastic deformation and other energy loss processes taking place during the operation of the machinery.
Ultrasonic activity is similar to noise but specifically refers to that part of the activity occurring at frequencies above the audible range (say above 20 kHz). Sometimes the term ultrasonics is used to denote the detection of structure borne activity at ultrasonic frequencies in which case it is largely interchangeable with the terms Acoustic Emission and stress waves.
Mechanical vibrations within materials and structures are associated with operating machinery and are produced as a result of out of balance forces, friction, impacts, cavitation, metal removal, crack growth, plastic deformation and other energy loss processes taking place during the operation of the machinery.
The early detection of machinery faults enables maintenance to be better planned and degrading machinery to be serviced, repaired or replaced with a minimum of disruption and cost. Whilst there are many ways in which the electrical signals form appropriate transducers and sensors can be processed for the purpose of machinery condition monitoring a common requirement is to characterise the sensor signal over a sufficiently long period to allow a representative assessment to be made. A difficulty therefore arises in determining the mechanical condition of machinery by monitoring sensor signals where machinery may operate for a period of time that is not sufficiently long to allow a representative measurement or assessment of machine condition to be made.
A difficulty in satisfactorily monitoring intermittently operating machinery is that there may be significant periods during which the machine speed is changing since neither the starting of a machine nor the stopping of a machine can be instantaneous. Furthermore depending on the nature of the machine there may also be actions associated with the starting and stopping process (e.g. brake, clutch and latch actions) which give rise to the generation of sensor signals which are not representative of signals generated during steady state operation.
In prior art methods of processing electrical signals responsive to the mechanical condition of operating machinery the electrical signals derived from one or more appropriate sensors or transducers are conditioned and processed in a wide variety of ways. The raw, conditioned or preliminarily processed electrical signals can be further processed in either the frequency or time domains as part of the process of assessing the mechanical condition of a machine.
In the frequency domain it is not untypical to ascribe certain frequencies or frequency bands as being related to fault mechanisms. For example it is not unusual to relate specific detected frequencies in the frequency spectrum of either the amplified vibration signal or the enveloped vibration or Acoustic Emission signal to pre-calculated defect repetition frequencies such as those related to the rolling element passing over a defect in the inner race or outer race of a bearing for example. In a similar way detected frequency components may be related to gear meshing frequencies. However analysis of such defect or defect repetition frequencies relies upon the precise relative timing of signal features throughout the whole of the signal period being processed and the presently disclosed invention is not considered appropriate to such frequency analysis.
Accordingly the present invention is concerned with those signal processing methods which are carried out in the time domain and do not rely specifically on the precise relative timing of features throughout the whole of the signal period being processed. Examples of time domain signal processing methods include but are not limited to rms, mean level, average level, average peak, crest factor, standard deviation, variance and kurtosis. Additional proprietary methods of time domain signal processing are also in widespread use and are included within the scope of the presently disclosed invention. Such time domain signal processing methods are not instantaneous but involve the processing of electrical signals over a finite period of time and hence for machinery monitoring purposes the machine must operate over this finite period of time whilst sensor signals are either stored or measurements are made on them as they are detected.
In practice an adequate period of machine running is required to (a) ensure that all relevant aspects of the moving and load bearing parts of the machine are included in the signal generation and (b) allow a statistically significant signal characterisation to be made of the detected signal. By way of example, in the case of a machine involving full rotation through 360 degrees it is not unusual for the signal measurement to be carried out over a period encompassing several complete revolutions of the machine part of interest.
Time domain methods of monitoring the mechanical condition of machinery that operates in a continuously rotating manner are well established using Acoustic Emission transducers and sensors, ultrasonic transducers and sensors, accelerometers and microphones, for example. Typically detected electrical signals are continuously inputted into a processing unit which has one or more outputs or displays of the signal characterisations of interest. These processed outputs may be interpreted in terms of their present values or from the trends that are revealed by observing changes in these values over time. They may also be used for providing warning or alarm outputs.
It is a common observation that with each of the above mentioned sensing technologies the magnitude of the detected electrical signal is influenced by the speed of the operation of the machine as well as the mechanical condition. In general signal magnitudes increase with increasing machine speed. Because of the influence of speed on the detected electrical signals it is generally the case that the most useful part of the sensor electrical signal from which to assess machine condition is that part associated with steady state running.
However the duration of a period of steady state running of an intermittently operating machine (excluding for example start-up, slow down, operational changes and stopped periods) may not allow a signal of sufficient duration to be detected to allow an effective assessment of mechanical condition to be made.
Hence a first difficulty in monitoring intermittently operating machinery is the presence of periods of time when the signal being detected does not correspond to steady state operation. A second difficulty in monitoring intermittently operating machinery occurs when some or all of the machine operations only have periods of steady state operation that are of too short a duration to allow an effective measurement related to the mechanical condition of the machine to be made.
To overcome these difficulties it can sometimes be arranged for the machine to be specially run in a continuous mode for an adequate period to allow a signal to be detected having a duration long enough to allow an effective measurement related to the mechanical condition of the machine to be made. Alternatively where some of the machine operations are of sufficiently long duration for an effective measurement to be made special arrangements may be made to only take a measurement during these longer operations.